title: Ensemble regularized linear discriminant analysis classifier for P300-based brain-computer interface.
abstract: This paper demonstrates a better classification performance of an ensemble classifier using a regularized linear discriminant analysis (LDA) for P300-based brain-computer interface (BCI). The ensemble classifier with an LDA is sensitive to the lack of training data because covariance matrices are estimated imprecisely. One of the solution against the lack of training data is to employ a regularized LDA. Thus we employed the regularized LDA for the ensemble classifier of the P300-based BCI. The principal component analysis (PCA) was used for the dimension reduction. As a result, an ensemble regularized LDA classifier showed significantly better classification performance than an ensemble un-regularized LDA classifier. Therefore the proposed ensemble regularized LDA classifier is robust against the lack of training data.
authors.forename: Akinari
authors.initials: A
authors.lastname: Onishi
authors.forename: Kiyohisa
authors.initials: K
authors.lastname: Natsume
mesh_terms: D062207:Brain-Computer Interfaces; D016002:Discriminant Analysis; D018913:Event-Related Potentials, P300; D006801:Humans; D025341:Principal Component Analysis